System, method and computer program product for predicting value of lead

ABSTRACT

Embodiments disclosed herein provide a solution in determining a lead value and making an introduction accordingly. In some embodiments, in response to a consumer&#39;s search request for a retail item within a geographical area, a decision system may obtain from a local database a list of dealers capable of provisioning the retail item—such as a new or used vehicle—at various locations within the geographical area. For each dealer, the system may calculate a dealer score across a plurality of tests and set a dollar value to an introduction utilizing the dealer score associated therewith. The performance measures of the tests may be normalized and adjusted utilizing a set of coefficients. The list of dealers may be sorted per dollar value of introduction and presented to the consumer. To provide more accurate dealer evaluations, the system may periodically reset the set of coefficients using sales data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims a benefit of priorityunder 35 U.S.C. 120 of the filing date of U.S. patent application Ser.No. 12/655,462, by inventors Noy et al., entitled “System, Method andComputer Program Product for Predicting Value of Lead” filed on Dec. 30,2009, which is hereby expressly incorporated by reference for allpurposes.

TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure relates generally to predicting the value of alead and, more particularly, to making introduction between a buyer anda seller based on the predicted value of the lead. Even moreparticularly, the present disclosure is related to optimizing theintroduction based on the likelihood of the buyer to purchase a thing ofvalue such as an automobile from the seller.

BACKGROUND OF THE DISCLOSURE

Geographic proximity is no longer the primary driver of auto purchases.In recent years, virtual dealerships have sprung up all over theInternet. When consumers go online to buy a car, there are usuallymultiple dealers that can sell a car to them. Intermediaryconsumer-oriented service providers typically have several automotivedealers in their system to which they can introduce to a customer.

Examples of the types of introductions may include an introduction forin-network dealers and an introduction for non-network dealers, and soon. In-network dealers may be those that have agreed to be in theintermediary service provider's network. For example, in-network dealersmay agree to pay the intermediary service provider a fee for anintroduction to a customer that ended up purchasing a vehicle, after thepurchase is made. This is sometimes referred to as Pay-Per-Sale.Currently, there is not a standardized way to operate in thismarketplace. Customers may visit multiple online solution providers,including automotive research sites, lead generation providers, etc.,for their vehicle purchasing needs.

Existing solutions are believed to be lacking or have drawbacks in atleast the following main areas: (1) leads are generally purchased inbulk where all leads in the same group or category have the same price;2) the determining factors on which dealers should be presented to whichcustomers are generally based on a simple set of considerations; and (3)little or no access to historical information across a wide consumerbase. As more and more consumers now surf the Internet to find deals,there is always room for improvement.

SUMMARY OF THE DISCLOSURE

Embodiments disclosed herein can provide a predictive value for eachintroduction between a potential buyer and a dealer within a dealernetwork. For illustrative purposes, embodiments disclosed hereindescribe a car dealer network where various types of vehicles may beavailable for purchase and/or lease. Those skilled in the art canappreciate that embodiments disclosed herein may be readily adapted forother types of dealer networks, including, but not limited to, a boatdealer network, a high end kitchen appliance dealer network, a bicycledealer network, a recreational vehicle network, etc.

Some embodiments disclosed herein may enable an intermediary onlinesolution provider to make a meaningful introduction that likely turnsinto a sale, on an item-by-item basis, between a potential customer anda dealer. A non-exhaustive list of factors, such as those listed below,may influence how a meaningful introduction can be made:

-   -   Which, if any, dealer(s) in the area should be introduced to a        particular customer?    -   If multiple dealers should be introduced to a customer, at what        order should the dealers be introduced?    -   Where multiple types of introduction are available, which dealer        should be introduced in which form?

To address these issues, some embodiments disclosed herein may beimplemented as a publicly-accessible Web site having suitable softwarerunning on one or more server machines for determining which dealer ordealers to introduce to a potential buyer, based on a predictive valueof such an introduction within a dealer network.

More specifically, a Web site implementing an embodiment disclosedherein may comprise the following functions, some of which may beoptional:

1) Recommendation Engine.

In some embodiments, the following variables may be used to generate adealer recommendation to a potential customer:

-   -   Price compression    -   Price distribution    -   Dealer location

In one embodiment, a dealer recommendation method may comprise 1)collecting data on price compression, price distribution, and dealerlocation; 2) applying weighting to each variable; and 3) tuning thevariables using one or more machine-learning techniques. In someembodiments, the data collected may include historical values over acertain period of time at or around the dealer location. In someembodiments, the weighting applied to each variable is tunable and/oruser definable.

2) Variable-Cost of Leads.

In some embodiments, every lead may have its own price. Some embodimentsdisclosed herein may determine a lead purchase price on a lead-by-leadbasis utilizing a plurality of factors, including, but not limited to,the purchase price set by a buyer. Some embodiments may utilize astatistical analysis by a buyer of information about the underlying leaditself to determine the statistical likelihood of a sale actuallyoccurring based on a particular lead.

In one embodiment, the statistical analysis by a buyer of informationmay be performed using the aforementioned dealer recommendation engineto recommend the lead to a particular car dealer and using thehistorical close rates on leads recommended using the dealerrecommendation engine.

3) What to Display and Optimized Order of Display.

Determine which dealers should be displayed to the buyer—with thehighest likelihood to submit a lead, and then purchase a car from thepresented dealer. For example, rather than using a single and simplerrubric such as price or distance, some embodiments may use predictivedata to determine the optimal order of presentation of dealers to apotential car buyer. This results in a better conversion of potentialcar buyers to leads and ultimately a higher monetization of the vehicleinquiry. Some embodiments may also present a blend of dealerships from adealer network and lead generation dealerships based on price. Thecohort of dealers may self-reinforce the sale to the user by skillfullypresenting comparisons to make the ultimate choice easier

4) Performance-Based Lead Sales.

Predict the likely cash value of a single selection point (i.e., asingle online customer looking to buy a single item). As each lead maybe priced individually, dealers may bid on the chance of beingintroduced to such a customer. This introduction-by-introduction basisallows the intermediary solution provider to place each individualintroduction in an open market competitive environment. For example,some embodiments may calculate and offer dynamic bids on leads in theopen market based on a potential car buyer's location and vehiclerequest. By contrast, the traditional approach for lead sellers is tooffer a single, static price for a lead. Conventional lead generationsystems usually provide lead pricing for a category of leads.

These, and other, aspects of the disclosure will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood,however, that the following description, while indicating variousembodiments of the disclosure and numerous specific details thereof, isgiven by way of illustration and not of limitation. Many substitutions,modifications, additions and/or rearrangements may be made within thescope of the disclosure without departing from the spirit thereof, andthe disclosure includes all such substitutions, modifications, additionsand/or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the disclosure. It should be notedthat the features illustrated in the drawings are not necessarily drawnto scale. A more complete understanding of the disclosure and theadvantages thereof may be acquired by referring to the followingdescription, taken in conjunction with the accompanying drawings inwhich like reference numbers indicate like features and wherein:

FIG. 1 is a simplified diagrammatic representation of one exampleembodiment of a system for predicting the value of a lead;

FIG. 2 is a simplified diagrammatic representation of one examplenetwork architecture in which embodiments disclosed herein may beimplemented;

FIG. 3 is a diagrammatic representation of one example embodiment ofsystem interaction;

FIG. 4 is a diagrammatic representation of one example embodiment of amethod of fine-tuning a plurality of tests utilized in evaluatingdealers known to an intermediary online solution provider;

FIG. 5 is a diagrammatic representation of one example embodiment of amethod of evaluating dealers within a range defined by a potentialbuyer;

FIG. 6 is a diagrammatic representation of one example embodiment of amethod of generating a list of dealers and setting a dollar value foreach introduction thereof; and

FIG. 7 is a diagrammatic representation of one example embodiment of asystem for transacting leads with third parties on an item-by-item basisin an open market.

DETAILED DESCRIPTION

The disclosure and the various features and advantageous details thereofare explained more fully with reference to the non-limiting embodimentsthat are illustrated in the accompanying drawings and detailed in thefollowing description. Descriptions of well-known hardware and softwarecomponents, programming languages and programming techniques are omittedso as not to unnecessarily obscure the disclosure in detail. Skilledartisans should understand, however, that the detailed description andthe specific examples, while disclosing preferred embodiments, are givenby way of illustration only and not by way of limitation. Varioussubstitutions, modifications, additions or rearrangements within thescope of the underlying inventive concept(s) will become apparent tothose skilled in the art after reading this disclosure.

Software implementing embodiments disclosed herein may be implemented insuitable computer-executable instructions that may reside on acomputer-readable storage medium. Within this disclosure, the term“computer-readable storage medium” encompasses all types of data storagemedium that can be read by a processor. Examples of computer-readablestorage media can include random access memories, read-only memories,hard drives, data cartridges, magnetic tapes, floppy diskettes, flashmemory drives, optical data storage devices, compact-disc read-onlymemories, and other appropriate computer memories and data storagedevices.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, process, article, orapparatus. Further, unless expressly stated to the contrary, “or” refersto an inclusive or and not to an exclusive or. For example, a conditionA or B is satisfied by any one of the following: A is true (or present)and B is false (or not present), A is false (or not present) and B istrue (or present), and both A and B are true (or present).

Additionally, any examples or illustrations given herein are not to beregarded in any way as restrictions on, limits to, or expressdefinitions of, any term or terms with which they are utilized. Insteadthese examples or illustrations are to be regarded as being describedwith respect to one particular embodiment and as illustrative only.Those of ordinary skill in the art will appreciate that any term orterms with which these examples or illustrations are utilized encompassother embodiments as well as implementations and adaptations thereofwhich may or may not be given therewith or elsewhere in thespecification and all such embodiments are intended to be includedwithin the scope of that term or terms. Language designating suchnon-limiting examples and illustrations includes, but is not limited to:“for example,” “for instance,” “e.g.,” “in one embodiment,” and thelike.

FIG. 1 is a simplified diagrammatic representation of example system 100comprising enterprise computing environment or network 130 of an onlinesolution provider Zag. As illustrated in FIG. 1, computer user orconsumer 110 may interact with Web site 140 to conduct their carresearch and perhaps purchase a new or used vehicle through Web site140. In one embodiment, the user's car buying process may begin when theuser directs a browser application running on the user's computer tosend a request over network 120 to Web site 140. The user's request maybe processed through decision system 160 coupled to Web site 140.

In some embodiments, decision system 160 may be capable of determiningthe user's likelihood to buy and the dollar value of certain dealersknown to decision system 160. In some embodiments, information aboutdealers known to decision system 160 is stored on database 150 coupledto decision system 160 as shown in FIG. 1.

In some embodiments, the decision system may be implemented as arecommendation engine capable of determining a list of dealers topresent to the user based on the user's likelihood to buy and the dollarvalue of the dealers presented. In one embodiment, the list of dealersmay be displayed to the user via a user interface.

In some embodiments, calculations by decision system 160 may be based oninformation from a plurality of system components, including data fromsales matching system 170, a list of available dealers and theirperformance history from database 150 and/or dealers 180, and individualbids offered by Lead Buyer Aggregators 190. Examples of specificcalculations by decision system 160 are described below with referenceto FIGS. 3-6.

FIG. 2 is a simplified diagrammatic representation of one examplenetwork architecture 200 in which embodiments disclosed herein may beimplemented. For simplification, a single client computer and a singleserver computer are shown in FIG. 2, representing an example hardwareconfiguration of data processing systems capable of bi-directionallycommunicating with each other over a public network such as theInternet. Those skilled in the art will appreciate that enterprisecomputing environment 130 may comprise multiple server computers andmultiple client computers may be bi-directionally coupled to Web site140 over network 120. Web site 140 may be hosted by server computer 210in enterprise computing environment 130.

Client computer 110 can include central processing unit (“CPU”) 111,read-only memory (“ROM”) 113, random access memory (“RAM”) 115, harddrive (“HD”) or storage memory 117, and input/output device(s) (“I/O”)119. I/O 119 can include a keyboard, monitor, printer, and/or electronicpointing device. Example of I/O 119 may include mouse, trackball,stylist, or the like. Client computer 110 can include a desktopcomputer, a laptop computer, a personal digital assistant, a cellularphone, or nearly any device capable of communicating over a network.Server computer 210 may have similar hardware components including CPU211, ROM 213, RAM 215, HD 217, and I/O 219.

Each computer shown in FIG. 2 is an example of a data processing system.ROM 113 and 213, RAM 115 and 215, HD 117 and 217, and database 150 caninclude media that can be read by CPU 111 and/or 211. Therefore, thesetypes of computer memories include computer-readable storage media.These memories may be internal or external to computers 110 and/or 210.

Portions of the methods described herein may be implemented in suitablesoftware code that may reside within ROM 213, RAM 215, HD 217, database150, or a combination thereof. In some embodiments, computerinstructions implementing an embodiment disclosed herein may be storedon a DASD array, magnetic tape, floppy diskette, optical storage device,or other appropriate computer-readable storage medium or storage device.A computer program product implementing an embodiment disclosed hereinmay therefore comprise one or more computer-readable storage mediastoring computer instructions translatable by CPU 211 to perform anembodiment of a method disclosed herein.

In an illustrative embodiment, the computer instructions may be lines ofcompiled C⁺⁺, Java, or other language code. Other architectures may beused. For example, the functions of server computer 210 may bedistributed and performed by multiple computers in enterprise computingenvironment 130. Accordingly, each of the computer-readable storagemedia storing computer instructions implementing an embodiment disclosedherein may reside on or accessible by one or more computers inenterprise computing environment 130.

In some embodiments, the various software components and subcomponents,including Web site 140, database 150, decision system 160, and salesmatching system 170, may reside on a single server computer or on anycombination of separate server computers. In some embodiments, some orall of the software components may reside on the same server computer.

FIG. 3 is a diagrammatic representation of one example embodiment ofsystem interaction. In embodiments disclosed herein, decision system 160may interact with a plurality of components to determine the bestresponse to a particular user's request so that a meaningfulintroduction can be made and likely be turned into a sale. In oneembodiment, these interactions may be housed in database 150 coupled todecision system 160.

In one embodiment, decision system 160 is engaged when consumer 110visits Web site 140 and conducts a search with a set of search criteria.Examples of search criteria may include zip code, a vehicle make, year,and model, etc.

As a specific example, in one embodiment, consumer 110 may provide Website 140 with a specific zip code and a particular vehicle make andmodel. In one embodiment, decision system 160 may search database 150and determine a dealer from which this specific user is most likely tobuy and returns a price that the user most likely will pay for thespecified vehicle. This is known as upfront pricing. Advantages ofupfront pricing may be found in an article by Scott Painter, “Car SalesLead Generation: Broken for Consumers, Broken for Dealers,” E-CommerceTimes, Sep. 15, 2008, 4 pages, the entire content of which isincorporated herein by reference.

In some embodiments, once consumer 110 selects a vehicle, indicatingtheir intention to buy, decision system 160 may operate to determinewhich dealers to display by identifying in-network dealers 180 nearconsumer 110. In one embodiment, decision system 160 may operate todetermine which dealers may buy this particular lead associated withconsumer 110. In one embodiment, decision system 160 may operate todetermine how much lead aggregators 190 may pay for this particularlead.

In some embodiments, if consumer 110 selects one or more dealers,decision system 160 may operate to submit consumer 110's lead to thosedealers 180 or lead aggregators 190. In one embodiment, sales matchingsystem 170 may operate to match submitted leads to sales reported bydealers via DMS Sales Data files. In one embodiment, if consumer 110buys a car, sales matching system 170 may operate to match that sale toa lead and update the stats stored in database 150 for use by decisionsystem 160 in subsequent calculations.

FIG. 4 is a diagrammatic representation of one example of a method offine-tuning a plurality of tests utilized in evaluating dealers known toan intermediary online solution provider. In some embodiments, method400 may comprise receiving sales data 420 at sales matching system 170from dealer 180. As described above, sales matching system 170 mayreceive sales data 420 from dealer 180 after a purchase from dealer 180is made by consumer 110. Sales matching system 170 may update database150 with sales data 420 received from dealer 180 or may provide salesdata 420 received from dealer 180 to decision system 160. In someembodiments, decision system 160 may periodically set and reset a set ofcoefficient weights utilizing sales data 420. This process can be usefulas well as practical for some applications. For example, in order tomake accurate predictions on the value of the leads on anintroduction-by-introduction basis, it may be desirable to have all thestatistical data initially. However, this is not a requirement. A systemimplementing an embodiment disclosed herein can begin with a set ofhistorical statistical data and improves/learns over time bymanipulating coefficient weights in view of sales data 420. As will bedescribed below with reference to FIG. 5, this set of coefficientweights can then be utilized to continually evaluate in-network dealers180.

For example, in one embodiment, decision system 160 may set and resetcoefficient weights using the results of normalization, the close ratefor in-network dealers, and the dollar value derived from selling leadsto dealers. In one embodiment, this periodic recalculation analyzessales data from sales matching system 170, stored leads from database150, and DMS sales data from dealers 180 and resets coefficient weightsaccordingly. In one embodiment, this process is done automatically andcontinuously.

In one embodiment, this periodic recalculation of coefficient weightsrepresents a learning loop for decision system 160 in evaluatingin-network dealers 180 over time. In some embodiments, decision system160 may operate to review the coefficients on a monthly basis. In someembodiments, decision system 160 may operate to re-evaluate dealers 180on a daily basis. As a specific example, a system implementing anembodiment disclosed herein may have 2,400 dealers, all of which wouldbe re-evaluated nightly and the system would be updated with the latestdata accordingly. In some embodiments, a dealer or dealership refers toan entity capable of provisioning a retail item such as a particular newcar of a certain make, model, and year—physical inventory of the same isnot required. One example could be that a buyer is interested inpurchasing a new mid-size sedan with a customized sports package byspecial order. Automotive dealerships typically do not keep a physicalinventory of all the retail items that they can provision for theircustomers. In embodiments disclosed herein, it is not necessary toreview against items in each dealership's physical inventory and thephysical inventory at a particular dealership has no effect on theevaluation of that dealership.

FIG. 5 is a diagrammatic representation of one example of a method ofevaluating dealers within a range defined by a potential buyer. Asillustrated in FIG. 5, in one embodiment, system 500 may evaluate allthe dealers available for a given user's search through a set of tests530. In this example, suppose an address or a zip code provided byconsumer 110 defines geographical area or range 510 and dealers 501,503, 505, 507, and 509 from in-network dealers 180 are located atvarious physical locations within area 510. Using a number ofcoefficients 535, represented by a₁, a₂, a₃, a₄, . . . , system 500 maynormalize a dealer's performance measures and variables to make moremeaningful comparisons for a given search request. The coefficients canbe modified and the effect of these changes can be measured as system500 learns to assign optimal weights to coefficients 535. Examplecalculations for normalization and coefficients are described below infurther detail with reference to FIG. 6. These coefficients may differfrom implementation to implantation. For example, in one market, thecoefficients may represent considerations such as Distance, PriceSavings, Sales to a particular Zip code, and the Close Rate of aparticular dealer. In another market, the coefficients may represent adifferent set of considerations. As another example, system 500 may testdifferent sets of coefficients representing different sets ofconsiderations for the same market. The coefficients may also differ forvarious reasons. For example, system 500 may test different coefficientsfor different makes of vehicle. Additional conditions may also affecthow system 500 tests the coefficients and/or what coefficients aretested.

In some embodiments, the system may consider primary variables andsecondary factors. An example of a primary variable may be the physicaldistance between the user and a particular dealership (i.e., thelocation of a vehicle desired by the user). In one embodiment, system500 may perform the dealer evaluation utilizing the following variables:

-   -   Variable 1: Physical distance between a user and a dealership        where the desired vehicle is located.    -   Variable 2: Historical sales in the last 60 days.    -   Variable 3: Dealer price offset (user's savings offered on a        partner site).    -   Variable 4: All sales to buyers within 5 miles of the search zip        code from this dealer. This may include direct sales and those        made through one or more intermediaries.    -   Variable 5: Average daily internally generated leads within a        distance range.    -   Variable 6: New car sales to dealer. This may include direct        sales and those made through one or more intermediaries.    -   Variable 7: Potential net revenue from revenue sharing with one        or more partner sites.

In one embodiment, variables 1 through 3 may be characterized as primaryvariables and variables 4 through 7 may be characterized as secondaryfactors. Other variables and/or factors may also be included.

FIG. 6 is a diagrammatic representation of one example of a method fordetermining a value of an introduction. Method or flow 600 may be usedby decision system 160 in response to consumer 110's search request fora Zip Code, Vehicle Make, and Vehicle Model combination. In someembodiments, decision flow 600 may send data to lead buyers 190 (step602) and wait for a response (step 620). In some embodiments, decisionflow 600 may send data to lead buyers 190 (step 602) and get allpossible dealers within area 510 from database 150 (step 604). Decisionflow 600 may normalize each dealer within area 510 across tests 530(step 606) and test multiple coefficients for each dealer (step 608).Decision flow 600 may then sort a list of dealers to which consumer 110is to be introduced (step 610) and set, for example, a dollar value foreach introduction (step 612). Decision flow 600 may blend lists, forexample, by dollar value per introduction (step 614). In one embodiment,the lists may blend with a response from step 620.

The specific set of tests performed by the underlying system on a set ofin-network dealers within a range defined by a user may vary fromimplementation to implementation. In some embodiments, the user isprovided with a set of choices via a user interface implementing anembodiment disclosed herein. Each choice may represent a particularcombination of variables. For example, one choice may include all makes,territories and program affinity groups (PAGS) and one choice may bebased on a specific make, model, and trim package.

In embodiments disclosed herein, each introduction has a valueassociated therewith. In one embodiment, this value is a dollar value.The value of a particular introduction is determined based in part on aparticular dealer's score. In embodiments disclosed herein, each dealerwithin a search range may be scored utilizing the sum of multiplyingeach coefficient with its normalized value such that:

Dealer score=N₁×C₁+N₂×C₂+ . . . +N_(x)×C_(x), where N_(x) represents thenormalized value between 0 and 1 per test and C_(x) represents thecoefficient for that market/make (or other parameter) combination.

In this case, the dollar value for an introduction is then the productof the dealer score, the score confidence, and the dollar per car sold:

Dollar value for an introduction=dealer score×score confidence×dollarper car sold.

Example Calculations

As a specific example, all variables are normalized with the highestmember of the cohort (comparison group of dealers) receiving 1 and thelowest receiving 0. In one embodiment, coefficients for the variablesmay be derived by taking each variable score on a dealer basis andweighting according to the following scheme:

${{average}\mspace{14mu} ( {\sum\limits_{{variable}\mspace{14mu} 1\text{-}7}^{\;}\; \begin{matrix}\begin{matrix}{{variable}\mspace{14mu} i*1\mspace{14mu} {for}\mspace{14mu} {lead}} \\{{variable}\mspace{14mu} i*3\mspace{14mu} {for}\mspace{14mu} {sales}}\end{matrix} \\{{variable}\mspace{14mu} i*0\mspace{14mu} {for}\mspace{14mu} {no}\mspace{14mu} {lead}}\end{matrix}} )} = {{raw}\mspace{14mu} {\beta.}}$

In one embodiment, coefficients may be derived and normalized by:

$\frac{{average}\mspace{14mu} ( {{raw}\mspace{14mu} \beta \; x} )}{\min \mspace{14mu} ( {{raw}\mspace{14mu} \beta \; 1\text{:}\mspace{14mu} {raw}\mspace{14mu} \beta \; 7} )} = {\beta \; {x.}}$

In one embodiment, algorithm score may be calculated as follows:

${\gamma \mspace{14mu} ( {{dealer}\mspace{14mu} {score}} )} = {\sum\limits_{i = 7}^{i = 1}\; {( {\beta \; i*\alpha \; 1} ).}}$

FIG. 7 is a diagrammatic representation of one example embodiment of asystem for transacting leads with third parties on an item-by-item basisin an open market. In Open Market model 700, decision system 160 may becontacted by third party Web site 780 as a result of a consumer searchvia Web site 780 for a Zip Code, Vehicle Make, and Vehicle Modelcombination. In this example, decision system 160 may return a responseto third party Web site 780 with information on dealer coverage. Ifdealer coverage exists, decision system 160 may return dealerinformation, vehicle price, and the lead's dollar value.

Although the present disclosure has been described in detail herein withreference to the illustrative embodiments, it should be understood thatthe description is by way of example only and is not to be construed ina limiting sense. It is to be further understood, therefore, thatnumerous changes in the details of the embodiments disclosed herein andadditional embodiments will be apparent to, and may be made by, personsof ordinary skill in the art having reference to this description.Accordingly, the scope of the present disclosure should be determined bythe following claims and their legal equivalents.

What is claimed is:
 1. A method of determining a value of a lead andmaking an introduction based thereon, comprising: receiving at a servercomputer hosting a Web site a request from a browser application runningon a client device of a consumer, wherein the request containsinformation about a retail item and a geographical area; extracting theinformation about the retail item and the geographical area from therequest; obtaining from a database coupled to the server computer a listof dealers capable of provisioning the retail item at various locationswithin the geographical area; determining a value of making anintroduction between each of the dealers and the consumer; sorting thelist of dealers per their corresponding, individual value ofintroduction; and presenting the sorted list of dealers to the consumervia the client device.
 2. The method according to claim 1, furthercomprising: for each of the dealers, calculating a dealer score across aplurality of tests, wherein the value of making an introduction betweena dealer and the consumer is determined utilizing the dealer scoreassociated with the dealer.
 3. The method according to claim 2, whereincalculating a dealer score across a plurality of tests further comprisesnormalizing performance measures across the plurality of tests.
 4. Themethod according to claim 3, further comprising testing multiplecoefficients for each of the dealers.
 5. The method according to claim4, further comprising periodically resetting the coefficients.
 6. Themethod according to claim 1, further comprising periodicallyre-evaluating the list of dealers utilizing data from previous salesassociated therewith.
 7. The method according to claim 1, wherein theretail item is a new or used vehicle and wherein the information aboutthe retail item comprises make and model of the new or used vehicle. 8.A computer program product comprising at least one computer-readablestorage medium storing computer instructions translatable by a processorto perform: receiving at a server computer hosting a Web site a requestfrom a browser application running on a client device of a consumer,wherein the request contains information about a retail item and ageographical area; extracting the information about the retail item andthe geographical area from the request; obtaining from a databasecoupled to the server computer a list of dealers capable of provisioningthe retail item at various locations within the geographical area;determining a value of making an introduction between each of thedealers and the consumer; sorting the list of dealers per theircorresponding, individual value of introduction; and presenting thesorted list of dealers to the consumer via the client device.
 9. Thecomputer program product of claim 8, wherein the computer instructionsare further translatable by the processor to perform: for each of thedealers, calculating a dealer score across a plurality of tests, whereinthe value of making an introduction between a dealer and the consumer isdetermined utilizing the dealer score associated with the dealer. 10.The computer program product of claim 9, wherein the computerinstructions are further translatable by the processor to perform:normalizing performance measures across the plurality of tests.
 11. Thecomputer program product of claim 10, wherein the computer instructionsare further translatable by the processor to perform: testing multiplecoefficients for each of the dealers.
 12. The computer program productof claim 11, wherein the computer instructions are further translatableby the processor to perform: periodically resetting the coefficients.13. The computer program product of claim 8, wherein the computerinstructions are further translatable by the processor to perform:periodically re-evaluating the list of dealers utilizing data fromprevious sales associated therewith.
 14. A system for making anintroduction on a lead-by-lead basis, comprising: a processor; and atleast one computer-readable storage medium storing computer instructionstranslatable by the processor to perform: receiving at a server computerhosting a Web site a request from a browser application running on aclient device of a consumer, wherein the request contains informationabout a retail item and a geographical area; extracting the informationabout the retail item and the geographical area from the request;obtaining from a database coupled to the server computer a list ofdealers capable of provisioning the retail item at various locationswithin the geographical area; determining a value of making anintroduction between each of the dealers and the consumer; sorting thelist of dealers per their corresponding, individual value ofintroduction; and presenting the sorted list of dealers to the consumervia the client device.
 15. The system of claim 14, wherein the computerinstructions are further translatable by the processor to perform: foreach of the dealers, calculating a dealer score across a plurality oftests, wherein the value of making an introduction between a dealer andthe consumer is determined utilizing the dealer score associated withthe dealer.
 16. The system of claim 15, wherein the computerinstructions are further translatable by the processor to perform:normalizing performance measures across the plurality of tests.
 17. Thesystem of claim 16, wherein the computer instructions are furthertranslatable by the processor to perform: testing multiple coefficientsfor each of the dealers.
 18. The system of claim 17, wherein thecomputer instructions are further translatable by the processor toperform: periodically resetting the coefficients.
 19. The system ofclaim 14, wherein the computer instructions are further translatable bythe processor to perform: periodically re-evaluating the list of dealersutilizing data from previous sales associated therewith.
 20. The systemof claim 14, wherein the retail item is a new or used vehicle andwherein the information about the retail item comprises make and modelof the new or used vehicle.